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Background: We investigated the association between prenatal GDM exposure and offspring DNA methylation (DNAm) age at 3–10 years of age in the Tianjin GDM Observational Study. Methods: This study included 578 GDM and 578 non-GDM mother-child pairs. Children underwent an exam with anthropometric measurements and blood draw for DNAm analysis (Illumina 850 K array) at a median age of 5.9 years (range 3.1–10.2). DNAm age was calculated using two epigenetic clock algorithms (Horvath and Hannum). The residual resulting from regressing DNAm age on chronological age was used as a metric for age acceleration. Results: Chronological age was positively correlated with Horvath DNAm age (r = 0.53, p < 0.0001) and Hannum DNAm age (r = 0.38, p < 0.0001). Offspring age acceleration was higher in the GDM group than non-GDM group after adjustment for potential confounders (Horvath: 4.96 months higher, adjusted for sex, pre-pregnancy BMI, cell-type proportions, and technical bias, p = 0.0002; Hannum: 11.2 months higher, adjusted for cell-type proportions and technical bias, p < 0.0001). Increased offspring DNAm age acceleration was associated with increased offspring weight-for-age Z-score, BMI-for-age-Z-score, waist circumference, body fat percentage, subscapular skinfold, suprailiac skinfold, upper-arm circumference, and blood pressure; findings were stronger in the GDM group. Conclusions: We found that offspring of women with GDM exhibit accelerated epigenetic age compared to control participants, independent of other maternal factors. Epigenetic age in offspring was associated with cardiometabolic risk factors, suggesting that GDM and GDM-associated factors may have long-term effects on offspring epigenetic age and contribute to health outcomes.

Splicing factor mutations are recurrent genetic alterations in blood disorders, highlighting the importance of alternative splicing regulation in hematopoiesis. Specifically, mutations in splicing factor 3B subunit 1 (SF3B1) are implicated in the pathogenesis of myelodysplastic syndromes (MDS) and linked to a high-risk of leukemic transformation in clonal hematopoiesis (CH). SF3B1 mutations are associated with aberrant RNA splicing, leading to increased cryptic 3' splice site (ss) usage and MDS with ring sideroblasts phenotype. The study of mutant SF3B1-driven splicing aberrations in humans has been hampered by the inability to distinguish mutant and wildtype single cells in patient samples and the inadequate coverage of short-read sequencing over splice junctions. To overcome these limitations, we developed GoT-Splice by integrating Genotyping of Transcriptomes (GoT; Nam et al. 2019) with Nanopore long-read single-cell transcriptome profiling and CITE-seq (Fig. A). This allowed for the simultaneous single-cell profiling of protein and gene expression, somatic mutation status, and alternative splicing. Our method selectively enriched full-length sequencing reads with the accurate structure, enabling the capture of higher number of junctions per cell and greater coverage uniformity vs. short-read sequencing (10x Genomics; Fig. B, C). We applied GoT-Splice to CD34+ bone marrow progenitor cells from MDS (n = 15,436 cells across 3 patients; VAF: [0.38-0.4]) to study how SF3B1 mutations corrupt human hematopoiesis (Fig. D). High-resolution mapping of SF3B1 mutvs. SF3B1 wt hematopoietic progenitors revealed an increasing fitness advantage of SF3B1 mut cells towards the megakaryocytic-erythroid lineage, resulting in an expansion of SF3B1 muterythroid progenitor (EP) cells (Fig. E, F). Accordingly, SF3B1 mutEP cells displayed higher protein expression of erythroid lineage markers, CD71 and CD36, vs. SF3B1 wt cells (Fig. G). In these SF3B1 mutEP cells, we identified up-regulation of genes involved in regulation of cell cycle and checkpoint controls (e.g., CCNE1, TP53), and mRNA translation (eIFs gene family; Fig. H). Next, while SF3B1 mut cells showed the expected increase of cryptic 3' splicing vs. SF3B1 wt cells (Fig. I), they exhibited distinct cryptic 3' ss usage as a function of hematopoietic progenitor cell identity, displaying stage-specific aberrant splicing during erythroid maturation (Fig. J). In less differentiated EP cells, we observed mis-splicing of genes involved in iron homeostasis, such as the hypoxia-inducible factor HIF1A, and key regulators of erythroid cell growth, such as SEPT2. At later stages, we observed mis-splicing of BAX, a pro-apoptotic member of the Bcl-2 gene family and transcriptional target of p53, and erythroid-specific genes (e.g., PPOX). We further predicted 54% of the aberrantly spliced mRNAs to introduce premature stop codons, promoting RNA degradation through nonsense-mediated decay (NMD). In line with this notion, we observed a significant decrease in expression of NMD-inducing genes in SF3B1 mut vs . SF3B1 wtEP cells (Fig. K). Lastly, splicing factor mutations observed in CH subjects provide an opportunity to interrogate the downstream impact of SF3B1 mutations prior to development of disease. Like MDS, by applying GoT-splice to CD34+ progenitor cells from SF3B1 mut CH subjects (n = 9,007 cells across 2 subjects; VAF: [0.15-0.22]; Fig. L), we revealed increased mutant cell frequency in EP cells (Fig. M) with concomitant increased expression of genes involved in mRNA translation (Fig. N), consistent with SF3B1 mutation causing mis-splicing injury to translational machinery and ineffective erythropoiesis. Notably, CH patients already exhibited cell-type specific cryptic 3' ss usage in SF3B1 mut cells (Fig. O). In summary, we developed a novel multi-omics single-cell toolkit to examine the impact of splicing factor mutations on cellular fitness directly in human samples. With this approach, we showed that, while SF3B1 mutations arise in uncommitted HSCs, their effect on fitness increases with differentiation into committed EPs, in line with the mutant SF3B1-driven dyserythropoiesis phenotype. We revealed that SF3B1 mutations exert cell-type specific mis-splicing that leads to abnormal erythropoiesis. Finally, we demonstrated that the impact of SF3B1 mutations on EP cells begins before disease onset, as observed in CH subjects. Figure 1 Figure 1. Disclosures Dai: Oxford Nanopore Technologies: Current Employment. Beaulaurier: Oxford Nanopore Technologies: Current Employment. Drong: Oxford Nanopore Technologies: Current Employment. Hickey: Oxford Nanopore Technologies: Current Employment. Juul: Oxford Nanopore Technologies: Current Employment. Wiseman: Astex: Research Funding; Novartis: Consultancy; Bristol Myers Squibb: Consultancy; Takeda: Consultancy; StemLine: Consultancy. Harrington: Oxford Nanopore Technologies: Current Employment. Ghobrial: AbbVie, Adaptive, Aptitude Health, BMS, Cellectar, Curio Science, Genetch, Janssen, Janssen Central American and Caribbean, Karyopharm, Medscape, Oncopeptides, Sanofi, Takeda, The Binding Site, GNS, GSK: Consultancy. Abdel-Wahab: H3B Biomedicine: Consultancy, Research Funding; Foundation Medicine Inc: Consultancy; Merck: Consultancy; Prelude Therapeutics: Consultancy; LOXO Oncology: Consultancy, Research Funding; Lilly: Consultancy; AIChemy: Current holder of stock options in a privately-held company, Membership on an entity's Board of Directors or advisory committees; Envisagenics Inc.: Current holder of stock options in a privately-held company, Membership on an entity's Board of Directors or advisory committees.